Code for Conservative Q-Learning for Offline Reinforcement Learning (https://arxiv.org/abs/2006.04779)
Update (07/17): We have released a cleaner implementation of BEAR on top of rlkit at: https://github.com/rail-berkeley/d4rl_evaluations, which goes with the latest version of the D4RL paper. We would encourage all users to use this new implementation as compared to this repo. We made…
Author implementation of 'Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations'
albertwilcox/mcac की मुख्य विशेषताएं हैं: Offline RL Implementations।
albertwilcox/mcac के ओपन-सोर्स विकल्पों में शामिल हैं: aviralkumar2907/cql — Code for Conservative Q-Learning for Offline Reinforcement Learning (https://arxiv.org/abs/2006.04779). avisingh599/cog — This repository accompanies the following paper:. danieltakeshi/dcur — This is the code used for the paper:. eladsar/rbi — Implementation of distributed RL algorithms:. facebookresearch/vip — Jason Yecheng Ma 12 , Shagun Sodhani 1 Dinesh Jayaraman 2 , Osbert Bastani 2 , {Vikash Kumar 1 , Amy Zhang 1 }. aviralkumar2907/bear — Update (07/17): We have released a cleaner implementation of BEAR on top of rlkit at:…
